Literature has shown that there are problems and challenges in the development of ML-enabled systems [1]}[2]}[3]}[4]}. Requirements engineering (RE) is no stranger to this. The impact of incomplete/hidden requirements on overall system development is considered one of the most critical problems of RE in practice  [5]}, and the difficulty to specify complete requirements increases for ML-enabled systems, where requirements engineers are typically not aware of the concerns that should be considered for such specification. Some recent research papers have drawn the attention of researchers and practitioners on the fact that ML can benefit from RE  [6]}[7]}[8]}[9]}. However, current research on the intersection between RE and ML mainly focuses on using ML techniques to support RE activities rather than on extending existing requirements techniques or providing new ones to support ML-enabled systems. In line with this, the roadmap for the future of SE proposed by the Carnegie Mellon University Software Engineering Institute [10]} emphasizes that existing RE methods will need to be expanded to decouple ML problem and model specification from the system specification. Indeed, recent literature reviews [11]}[12]} show that topics such as identifying quality attributes, specifying them, and understanding how they can be analyzed are not well-established and researched in the context of ML.
